This article investigates the problem of data-driven cooperative tracking for a class of multi-agent linear systems under imperfect wireless communication. An autonomous Internet of Things predictive control application is designed to drive a robot with one wheel. The proposed methodology has been developed using Revolutionary Internet of Things Operating System running on STM32 and radio frequency communication shields over the User Datagram Protocol. To evaluate the performance of the predictive control algorithm, the User Datagram Protocol has been used due to the high number of packet losses in the communication channel. A robust analysis of Internet of Things technology among agents, combined with a network predictive control strategy against packet loss, limited bandwidth and attack links is carried out. The main feature of this methodology is that it is possible to achieve consensus monitoring and stability of closed-loop control systems. The efficiency of the proposed design approach is demonstrated by several experimental scenarios.

This paper adresses the design of a new extension of fast nonlinear model predictive control (NMPC) for parallel manipulators. The developed controller is based on a parameterized NMPC, a fast gradient solver and a proportional derived controller (PD). The main motivation behind the proposed approach is to improve the tracking performance of fast parallel manipulators and reduce the computation time per control iteration. This control scheme is faster, in terms of computing time, than the classical NMPC and ensures the robustness of the resulting closed-loop system. To demonstrate the effectiveness of the proposed controller, real time experiments are performed on a 4-DOF parallel kinematic manipulator called VELOCE.